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A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China
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1-s2.0-S1201971220322797-main.pdf | Published version | 2.15 MB | Adobe PDF | View/Open |
Title: | A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China |
Authors: | Fu, H Wang, H Xi, X Boonyasiri, A Wang, Y Hinsley, W Fraser, KJ McCabe, R Olivera Mesa, D Skarp, J Ledda, A Dewé, T Dighe, A Winskill, P Van Elsland, SL Ainslie, KEC Baguelin, M Bhatt, S Boyd, O Brazeau, NF Cattarino, L Charles, G Coupland, H Cucunubá, ZM Cuomo-Dannenburg, G Donnelly, CA Dorigatti, I Eales, OD Fitzjohn, RG Flaxman, S Gaythorpe, KAM Ghani, AC Green, WD Hamlet, A Hauck, K Haw, DJ Jeffrey, B Laydon, DJ Lees, JA Mellan, T Mishra, S Nedjati Gilani, G Nouvellet, P Okell, L Parag, KV Ragonnet-Cronin, M Riley, S Schmit, N Thompson, HA Unwin, HJT Verity, R Vollmer, MAC Volz, E Walker, PGT Walters, CE Waston, OJ Whittaker, C Whittles, LK Imai, N Bhatia, S Ferguson, NM |
Item Type: | Journal Article |
Abstract: | Objectives: This data collation effort aims to provide a comprehensive database to describe the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19)across main provinces in China. Methods: From mid-January to March 2020, we extracted publicly available data on the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted a descriptive analysis of the epidemics in the six most-affected provinces. Results: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends were different across provinces. Compared to Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as local transmission of COVID-19 declined, switching the focus of measures to testing and quarantine of inbound travellers could help to sustain the control of the epidemic. Conclusions: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database with these indicators and information on control measures provides useful source for exploring further research and policy planning for response to the COVID-19 epidemic. |
Issue Date: | Jan-2021 |
Date of Acceptance: | 23-Oct-2020 |
URI: | http://hdl.handle.net/10044/1/83522 |
DOI: | 10.1016/j.ijid.2020.10.075 |
ISSN: | 1201-9712 |
Publisher: | Elsevier |
Start Page: | 463 |
End Page: | 471 |
Journal / Book Title: | International Journal of Infectious Diseases |
Volume: | 102 |
Copyright Statement: | © 2020 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Sponsor/Funder: | Medical Research Council (MRC) Abdul Latif Jameel Foundation Medical Research Council Wellcome Trust The Royal Society Medical Research Council Medical Research Council (MRC) Bill & Melinda Gates Foundation Imperial College Healthcare NHS Trust- BRC Funding The Academy of Medical Sciences Bill & Melinda Gates Foundation National Institute for Health Research UK Research and Innovation Medical Research Council (MRC) |
Funder's Grant Number: | MR/R015600/1 MR/R015600/1 213494/Z/18/Z DH140134 MR/R015600/1 MR/K010174/1B 1606H5002/JH6 RDA02 SBF004/1080 RES- -62388 NIHR200908 MR/V038109/1 EP/V520354/1 |
Keywords: | Science & Technology Life Sciences & Biomedicine Infectious Diseases COVID-19 China Epidemic Control measure Case fatality ratio Contact COVID-19 Case fatality ratio China Contact Control measure Epidemic COVID-19 China Contact Tracing Databases, Factual Humans SARS-CoV-2 Humans Contact Tracing Databases, Factual China COVID-19 SARS-CoV-2 Microbiology 0605 Microbiology 1108 Medical Microbiology 1117 Public Health and Health Services |
Publication Status: | Published |
Article Number: | THEIJID-D-20-03362R1 |
Online Publication Date: | 2020-10-31 |
Appears in Collections: | Department of Infectious Diseases Statistics Faculty of Medicine Imperial College London COVID-19 School of Public Health Faculty of Natural Sciences Mathematics |
This item is licensed under a Creative Commons License